Compute and extract model parameters. See the documentation for your object's class:
Correlations, t-tests, ... (htest, pairwise.htest)
ANOVAs (aov, anova, afex, ...)
Regression models (lm, glm, survey, ...)
Additive models (gam, gamm, ...)
Zero-inflated models (hurdle, zeroinfl, zerocount)
Multinomial, ordinal and cumulative link models (bracl, multinom, mlm, ...)
Other special models (model.avg, betareg, glmx, ...)
Mixed models (lme4, nlme, glmmTMB, afex, ...)
Bayesian tests (BayesFactor)
Bayesian models (rstanarm, brms, MCMCglmm, ...)
PCA and FA (psych)
CFA and SEM (lavaan, blavaan)
Cluster models (k-means, ...)
Meta-Analysis via linear (mixed) models (rma, metaplus, metaBMA, ...)
Hypothesis Testing (glht)
model_parameters(model, ...)parameters(model, ...)
Statistical Model.
Arguments passed to or from other methods. Non-documented arguments are digits, p_digits and ci_digits to set the number of digits for the output. See 'Examples' in model_parameters.default.
A data frame of indices related to the model's parameters.
Throughout the parameters package, we decided to label the residual
degrees of freedom df_error. The reason for this is that these degrees
of freedom not always refer to the residuals. For certain models, they refer
to the estimate error - in a linear model these are the same, but in - for
instance - any mixed effects model, this isn't strictly true. Hence, we
think that df_error is the most generic label for these degrees of
freedom.
Note that the interpretation of interaction terms depends on many
characteristics of the model. The number of parameters, and overall
performance of the model, can differ or not between a * b
a : b, and a / b, suggesting that sometimes interaction terms
give different parameterizations of the same model, but other times it gives
completely different models (depending on a or b being factors
of covariates, included as main effects or not, etc.). Their interpretation
depends of the full context of the model, which should not be inferred
from the parameters table alone - rather, we recommend to use packages
that calculate estimated marginal means or marginal effects, such as
modelbased, emmeans or ggeffects. To raise
awareness for this issue, you may use print(...,show_formula=TRUE)
to add the model-specification to the output of the
print() method for model_parameters().
Standardization is based on standardize_parameters().
In case of standardize = "refit", the data used to fit the model
will be standardized and the model is completely refitted. In such cases,
standard errors and confidence intervals refer to the standardized coefficient.
standardize_names() to rename
columns into a consistent, standardized naming scheme.